•Under dynamic loading, there is a positive correlation between the variation magnitude of SMFL intensity and the variation magnitude of stress–strain in rebars.•The increase in the number of cyclic ...loading enhances the stability of the force-magnetic coupling effect.•The proportional ultimate strength of the rebar leads to a non-monotonic variation of SMFL intensity. The variation rate of SMFL intensity accurately reflects the range of rebar stress.•The SMFL monitoring method can accurately assess the fluctuations in the load-bearing capacity level of RC bridges under service conditions.
This study investigates the use of Self-Magnetic Flux Leakage (SMFL) monitoring to assess the load-bearing capacity of RC bridges under vehicular loading. Cyclic tensile experiments of HRB400 rebars were conducted and the SMFL intensity was monitored. The integrated vector value of SMFL intensity BC was introduced to analyze the characteristics of the rebar force-magnetic coupling effect. Moreover, SMFL monitoring experiments were conducted on Gaojia Garden Bridge to evaluate the engineering applicability of the method. The results show that the mechanical indexes have a positive correlation with SMFL intensity during rebar cyclic loading. The increase in the cycle number drives the stability of the force-magnetic relationship. The rebar proportional ultimate strength σpl is an important factor affecting the BC variation. When the stress exceeds σpl, the rising process of BC curve retreats. The rebar stress range can be accurately determined based on the change rate of SMFL intensity. In the testing of Gaojia Garden Bridge, vehicles loading and unloading contribute to the phased increase and monotonic decrease of BC curves, respectively. Comparing the FEM simulation results, the linear characteristic relationships between BC and |με| as well as σ are obtained, which verifies the engineering applicability and accuracy of SMFL monitoring method. This research provides a new approach for the non-destructive quantitative detection and test application of RC bridge load-bearing capacity.
The challenge of the research work presented in the paper is to combine the growing interest in monitoring the health condition of existing bridge heritage through systematic and periodic visual ...inspections with automated recognition of typical bridge defects, which can greatly facilitate the assessment of defect evolution over time. The study focused on the automated identification of defects in existing Reinforced Concrete (RC) bridges exploiting different Deep Learning (DL) approaches and techniques to interpret the obtained predictions. Ensuring the safety of infrastructures is typically a technical and economic issue. Still, in the case of the engineering infrastructure heritage, there are existing bridges and viaducts with a high historical, cultural, and symbolic value. For them, accurate knowledge and characterization of possible degradation processes become particularly important in order to define intervention strategies that combine safety and conservation requirements. With the aim to develop systematic and non-invasive investigation protocols for continuous and effective control of defects and their evolution, a database of existing RC bridge defect images was collected, and the most recurrent defect typologies were classified by domain experts. Some existing Convolutional Neural Networks (CNNs) algorithms were applied to the dataset for automatically recognizing all defects, but the specific novel contribution of the research work is the interpretation of the obtained results in a form that is humanly explainable and directly implementable in new tools for bridge inspections. To interpret the results, Class Activation Maps (CAMs) approaches were employed within available eXplainable Artificial Intelligence (XAI) techniques, which allow to observe the activation zones and nearly perfectly highlight the type of specific defect in a given image. The obtained results, besides suggesting which network works better than others and if the specific defect is effectively recognized, have been evaluated through a quasi-quantitative procedure that compared a qualitative assessment of the CNNs models reliability with two novel indexes representing new explaining metrics of the obtained results. In the end, the outcomes of the proposed study were observed also in a real-life case study. The proposed discussion opens new scenarios in the application of these techniques for supporting road management companies and public organizations in the evaluation of the road networks health state.
•Application of AI-based techniques for Risk management of existing Bridge Heritage.•Eight existing CNN models were used to automatically identify all defects.•Two Class Activation Maps approaches were used to visually explain obtained results.•A quasi-quantitative interpretation of results was provided through two novel indexes.•The methodology was tested on a real existing RC bridge, characterized by heritage value.
Aging infrastructures require huge budgets to preserve their functionality and the lack of effective maintenance leads to increasing deterioration and therefore higher repair costs. For these ...reasons, assessing the condition of infrastructures becomes mandatory, with particular attention to the ones still in service even when their life limit has been exceeded. In particular, this problem is really important in Countries, like Italy, in which there are several operating bridges at the end of their service life. In this framework, many companies responsible for the management of road networks have turned their interest towards Bridge Management Systems (BMS). This paper presents a fast and low‐cost method for condition rating of reinforced concrete bridges. The proposal is based on visual inspection and non‐destructive testing. A specific parameter is designed to take into account the mechanical degradation of materials and the damage location at the structural sub‐component level. Some benchmark case studies have been discussed in order to compare the proposed method with other approaches available in literature.
Mexico highway network has more than 14,000 bridges. Most of them are reinforced concrete (RC) structures. The bridges design process incorporates the use of an overstrength factor that is not ...justified and has received little attention in published works. Mexican regulations allow using an overstrength factor for buildings in the range of 2–3, to reduce the design spectra as a function of the selected seismic behavior factor. However, for bridges, a single factor of 1.5 is proposed independent of any design parameter. The bridges in Mexico are mostly simply supported structures with maximum span lengths of 50 m. A relevant and distinctive aspect of the bridges designed in Mexico is the large load amplitudes of the trucks used to define the live load and the high seismic activity in the country. This study determines overstrength factors of a family of medium‐length RC bridges composed of simply supported superstructures and substructure made up of single and multi‐column RC piers. Non‐linear dynamic analyzes using a set of 80 accelerograms were carried out. The results show that the height of the bridges and their seismic location are relevant parameters in the overstrength of the structures. Finally, analytical expressions are proposed to assess the overstrength factors of a very common bridge typology in Mexico and the world.
Rapid and accurate post‐earthquake damage evaluation of regional reinforced concrete (RC) bridges is a key issue for assessing the seismic resilience of cities and communities. Especially, RC bridges ...are susceptible to the aggressive environment, which can induce time‐dependent aging effects such as corrosion, and thus, it should be considered in the assessment. This paper presents an approach for regional seismic performance assessment of RC bridges in a life‐cycle context based on machine‐learning techniques. The life‐cycle seismic demand and capacity of the bridges are, firstly, obtained by the elaborated numerical model, which includes the deterioration induced by the aging corrosion effect. Then, the tagging‐based damage state (green, yellow, or red) is easily obtained by comparing the pairs of demand and capacity through machine learning. Four hundred and eighty bridge models are generated to develop the machine‐learning models and the performance of the machine learning models is evaluated. Results show that the extreme gradient boosting (XGBoost) model has the best performance, which has an accuracy of 81% in predicting the damage states. The proposed approach is demonstrated with a single bridge example and bridges in a sample region. It is shown that the machine learning model can accurately predict the post‐earthquake damage states of the single bridge, and it can also rapidly assess the damage states of the bridges in the sample region. Approximately 30% bridges in the sample region will experience damage states shift after 100 years, which highlights the importance of considering the aging effects on the post‐earthquake damage assessment of bridges.
•The corrosion effects due to the carbonation phenomenon are evaluated considering existing RC bridges.•Multi-modal pushover analysis was conducted in order to evaluate the influence of the corrosion ...effects on the seismic performance of existing RC bridges considering two different collapse mechanisms: the ductile collapse mechanism and the brittle collapse mechanism.•The results are summarized in terms of appropriate risk indices to highlight the evolution of the collapse mechanisms during the life of the structure.
Recent collapse events of existing reinforced concrete bridges have increased the attention on the mandatory and suitable maintenance of these strategic constructions. In fact, most of these failures were due to an inadequate scheduling of maintenance interventions. One of the main issues concerning the load-bearing capacity of existing reinforced concrete structures is related to the steel reinforcement corrosion caused by carbonation phenomenon. Such aspect should not be even more overlooked considering the strategic role of infrastructures like the bridges of the Italian motorway network, mainly built around the 1960′s and widely used even right now. Consequently, reinforced concrete bridges require the execution of maintenance interventions in order to guarantee an adequate safety level under both serviceability conditions and exceptional loads, also considering that they were often designed without taking into account seismic actions.
This paper investigates the seismic performance of five existing reinforced concrete bridges under several corrosion scenarios of piers steel reinforcement caused by carbonation phenomenon. In particular, three different corrosion levels (slight, moderate and high) are considered by analysing the evolution of the phenomenon effects for a structure lifetime equal to 75 years. The seismic vulnerability is evaluated by defining appropriate risk indices expressed in terms of peak ground acceleration and corresponding return period. The risk indices are determined by performing modal pushover analyses on finite element models of the bridges, considering the corrosion effects in terms of steel rebars cross section reduction. Some correlations between corrosion levels and risk indices are drawn.
•Estimation of damping ratios on a short-span RC bridge with half-joints beam through AVTs in in-service conditions.•Minimum signal length to minimize the bias in the damping estimation.•Analysis of ...the damping with a fixed-length moving window over the entire vibration signatures.•Correlation between signals properties and estimated damping parameters.
Experimental tests for dynamic identification of reinforced concrete (RC) bridges by means of Operational Modal Analysis (OMA) are increasingly used in common engineering practice. Nevertheless, especially when measurements are carried out under in-service conditions (i.e. under traffic induced vibrations), some drawbacks should be carefully considered, especially in the damping-ratio quantification. As a matter of fact, the estimation is affected by several factors: i) the length of the signals, ii) the non-stationarity of the input process, and iii) the dependence on the vibration amplitude. Even if the damping ratio is a key parameter in the bridge dynamics, a major part of these aspects has not been yet fully investigated to estimate reliable values.
Starting from a dynamic test program on a short-span RC bridge with half-joints in Italy, this paper investigates the issues mentioned above for the damping ratio estimation of the first two modes focusing on: i) the influence of the signal length, ii) the effects of the signals properties, and iii) their correlation with the vibration amplitude. Both the Stochastic Subspace Identification technique fed with the signal covariance (SSI-cov) and the Random Decrement technique (RD) have been used to compute the damping ratios from the collected signals. This paper shows how a convergence of the results cannot be attained by simply increasing the sample size, suggesting that the nature of the vibration itself influences the damping values. A negative, although weak, correlation between the damping ratio and the power of the signals indicates that several factors play a crucial role in the damping estimation in the short span RC bridges with half-joints.
Corrosion-induced deterioration of material properties can noticeably affect the seismic performance of coastal reinforced concrete (RC) bridges in aggressive marine environments, causing significant ...adverse economic and environmental consequences post extreme events. This study systematically investigates the time-dependent seismic performance, resilience, and sustainability of coastal RC bridges. For that purpose, a three-dimensional (3D) nonlinear finite element (FE) framework is developed and combined with the formulation of performance-based earthquake engineering (PBEE) and an economic input-output life cycle assessment (LCA) approach. Seismic resilience and robustness of the coastal RC bridges are subsequently evaluated in terms of the post-earthquake repair cost, repair time, and carbon footprint quantified by CO2. Within this framework, additional analyses are conducted to explore possible retrofit measures, including the use of fiber-reinforced polymer (FRP) reinforcement on the sustainability and resilience of the retrofitted RC bridges. For the presented scenario, it is shown that wrapping bridge columns with FRP composites can effectively increase the moment capacity, thus improving resilience and sustainability by reducing as much as 650 Mg of CO2 for this specific bridge. Overall, the derived insights highlight the need for sustainability, resilience, and potential retrofit analysis of coastal RC bridges in seismically active regions affected by corrosion-induced deterioration.
•A nonlinear 3D FE framework for coastal RC bridges is developed and combined with the PBEE formulation and EIO-LCA approach.•Seismic resilience and sustainability of RC bridges under aggressive marine environments and earthquakes are investigated.•Effectiveness of FRP composites to potentially enhance the performance of bridge columns is thoroughly explored.•Wrapping coastal bridge columns with FRP composites can effectively improve seismic resilience and sustainability.
•A computationally non-expensive simulation scheme is investigated for RC bridges.•Multiple bridges and real ground motions are employed for reliable conclusions.•Statistical characterization of pier ...cross sections requires 50–100 realizations.•Failure probability of RC bridge piers requires at least 200 realizations.•Latin Hypercube is resilient to high dispersion levels and independent repetitions.
The seismic response of RC structures is dictated by a number of aleatory and epistemic uncertainties, related to material properties and seismic hazard. Typically, when following a probabilistic approach, the different variables are statistically defined using a random simulation tool that will take into account the different sources of uncertainty. This manuscript addresses the seismic reliability of RC bridge structures, particularly through the assessment of a random sampling scheme based on the Latin Hypercube algorithm for numerical simulation of material and ground motion properties. An optimal use of such sampling technique is thus sought so as to ensure higher accurateness in the calculation of the failure probability of a set of different existing bridges, through a relatively simple framework. The study demonstrates the robustness of the Latin Hypercube algorithm, which secured accuracy of the estimated probability of failure for both regular and irregular configurations. Recommendations to researchers and practitioners are made accordingly, particularly with respect to the minimal sampling size, which does not need to exceed one to two hundred realisations.
